We first evaluate the computational complexity of the LBL receiver.The computational complexity of the proposed receiver; the representative of best linear receivers, MMSE [1]} scheme; the representative of Bayesian receivers EP [2]}, [3]} and AMP [4]} schemes; and the exhaustive search based ML scheme [5]} are tabulated in Table I. The table indicates that the computational complexity of the proposed receiver increases linearly with the number of antennas, \(N\)  and users, \(K\)  by avoiding matrix inversion operations. This is in contrast to all other receivers whose computational complexity grow exponentially with \(N\)  and/or \(K\) . We have also observed that the number of iterations, \(T\)  needed for the EP, AMP, and LBL receivers are similar. However, the convergence rates of the AMP and EP receivers are not shown in this paper due to a space limitation. Therefore, the proposed receiver has a significantly lower processing latency and thus more suitable for URLLC data traffic. For instance, for \(K=64, N=192, \text{and } T=10\) , LBL receiver complexity is 19 times lower than MMSE receiver, and 192 times lower than EP receiver. We conclude that by eliminating the matrix inversion operations, we can obtain a significantly lower computational complexity and thus much lower latency.
